Recent years have seen a rapid proliferation in the use of digital devices for capturing digital images. Indeed, in addition to utilizing digital devices to communicate, generate documents, and play music, individuals and businesses now routinely utilize smartphones, cell phones, digital cameras, or other mobile digital devices to capture digital images relating to employment, entertainment, and other aspects of daily life.
Recently, it has also become increasingly common for individuals to use smartphones (or other mobile devices) to capture digital images of paper documents. For example, individuals routinely capture digital images of notes, receipts, business cards, invoices, reports, medical cards, etc. Utilizing mobile devices, individuals and businesses can easily capture, store, and utilize documents in digital format.
Although it is increasingly common for individuals to convert physical documents to digital images utilizing mobile digital image capturing devices, resulting digital images often have a number of problems. Indeed, documents captured in digital images via mobile devices are often unclear and difficult to see and/or read. For example, capturing digital images of documents utilizing smartphones (or other mobile devices) typically results in digital images with inconsistent lighting due to non-uniform light sources and shadows (e.g., a shadow from a hand, arm, or head of a person capturing the digital image). Similarly, white documents captured in a digital image with a smartphone often have a grayish hue (i.e., the background does not appear white in the resulting digital image). Moreover, documents captured utilizing mobile devices often result in digital images containing blurred text due to incorrect focus and/or lens limitations.
Some conventional digital image processing systems seek to solve these problems with complex machine learning classifiers. Although such conventional digital image processing systems identify and correct dark regions, they are often computationally exhausting (i.e., they require significant computational power to train and utilize a classifier). Moreover, such systems often have difficulty working with images containing layered or diffused shadows, which are typical with digital images captured via a smartphone or digital camera. Similarly, such conventional systems often require a calibrated camera and/or light sensor data, which are not generally available when individuals and businesses capture digital images.
These and other problems exist with regard to current techniques for enhancing digital images.